Evaluation of hydrologic components of community land model 4 and bias identification

Du, Enhao, Vittorio, Alan Di and Collins, William D. ORCID: https://orcid.org/0000-0002-4463-9848 (2016) Evaluation of hydrologic components of community land model 4 and bias identification. International Journal of Applied Earth Observation and Geoinformation, 48. pp. 5-16. ISSN 1569-8432

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Abstract

Runoff and soil moisture are two key components of the global hydrologic cycle that should be validated at local to global scales in Earth System Models (ESMs) used for climate projection. We have evaluated the runoff and surface soil moisture output by the Community Climate System Model (CCSM) along with 8 other models from the Coupled Model Intercomparison Project (CMIP5) repository using satellite soil moisture observations and stream gauge corrected runoff products. A series of Community Land Model (CLM) runs forced by reanalysis and coupled model outputs was also performed to identify atmospheric drivers of biases and uncertainties in the CCSM. Results indicate that surface soil moisture simulations tend to be positively biased in high latitude areas by most selected CMIP5 models except CCSM, FGOALS, and BCC, which share similar land surface model code. With the exception of GISS, runoff simulations by all selected CMIP5 models were overestimated in mountain ranges and in most of the Arctic region. In general, positive biases in CCSM soil moisture and runoff due to precipitation input error were offset by negative biases induced by temperature input error. Excluding the impact from atmosphere modeling, the global mean of seasonal surface moisture oscillation was out of phase compared to observations in many years during 1985–2004. The CLM also underestimated runoff in the Amazon, central Africa, and south Asia, where soils all have high clay content. We hypothesize that lack of a macropore flow mechanism is partially responsible for this underestimation. However, runoff was overestimated in the areas covered by volcanic ash soils (i.e., Andisols), which might be associated with poor soil porosity representation in CLM. Our results indicate that CCSM predictability of hydrology could be improved by addressing the compensating errors associated with precipitation and temperature and updating the CLM soil representation.

Item Type:	Article
Additional Information:	Publisher Copyright: © 2015
Uncontrolled Keywords:	bias test,clm4,historical evaluation,runoff,surface soil moisture,global and planetary change,earth-surface processes,computers in earth sciences,management, monitoring, policy and law,sdg 13 - climate action,sdg 15 - life on land ,/dk/atira/pure/subjectarea/asjc/2300/2306
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	06 Jul 2026 14:08
Last Modified:	12 Jul 2026 23:03
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103635
DOI:	10.1016/j.jag.2015.03.013

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